MKAljermy/apex-decision-extraction
VISIONConcurrent Unit Cost:1Model Size:7.9BQuant:FP8Context Size:32kTool Calling:SupportedPublished:Jul 28, 2026License:apache-2.0Architecture:Transformer Open Weights Featherless Exclusive Cold
MKAljermy/apex-decision-extraction is a 7.9 billion parameter instruction-tuned language model developed by MKAljermy. Finetuned from unsloth/gemma-4-e4b-it-unsloth-bnb-4bit, it leverages Unsloth for accelerated training. This model is optimized for decision extraction tasks, offering a 32768 token context length for processing extensive inputs.
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Model Overview
MKAljermy/apex-decision-extraction is a 7.9 billion parameter language model developed by MKAljermy. It is an instruction-tuned variant, finetuned from the unsloth/gemma-4-e4b-it-unsloth-bnb-4bit base model. A key aspect of its development is the utilization of Unsloth and Huggingface's TRL library, which enabled a 2x faster training process.
Key Capabilities
- Instruction Following: Designed to respond effectively to instructions, making it suitable for various NLP tasks.
- Decision Extraction: Optimized for identifying and extracting decisions from text, which is its primary intended use case.
- Extended Context: Features a substantial context length of 32768 tokens, allowing it to process and understand longer documents or conversations.
- Efficient Training: Benefits from Unsloth's optimizations, indicating a focus on performance and resource efficiency during its development.
Good For
- Applications requiring the identification and extraction of specific decisions or action items from unstructured text.
- Tasks that benefit from a model capable of handling long input sequences due to its large context window.
- Developers seeking an instruction-tuned model with a focus on practical information extraction.